/EnrichmentScoreCalculator

EnrichmentScoreCalculator

This project contains a Python script used to calculate enrichment scores for genes based on five different datasets. The enrichment scores represent the significance of the genes across different biological conditions or contexts.

Table of Contents

  1. Dependencies
  2. Datasets
  3. How to Run
  4. Code Documentation
  5. Output
  6. Final Remarks

Dependencies

The following Python libraries are required:

pandas
gseapy
matplotlib
os
numpy
seaborn
scipy
sys

Datasets

The datasets utilized in this project are significant enrichment files with FDR <= 0.05, including:

  1. Compendium
  2. Embryo Development
  3. Post-embryonic Development
  4. Cell Types
  5. Tissues

Each dataset needs to be pre-processed and stored in CSV format.

How to Run

  1. Ensure all dependencies are installed.
  2. Place the datasets in appropriate paths.
  3. Run the Python script.

Code Documentation

Importing Modules

Import the necessary modules required for reading datasets, performing computations, and plotting.

import pandas as pd
import gseapy as gp
import matplotlib.pyplot as plt
import os
import numpy as np
import seaborn as sns
from scipy import stats as st

Setting Base Directory

Establish the base directory containing the datasets.

Base_dir='/data/nandas/AllDatasets'
os.chdir(Base_dir)

Reading Datasets

Load the CSV datasets into Pandas DataFrames.

Compendium=pd.read_csv("Path to Compendium Dataset", index_col=0)
EmbDev=pd.read_csv("Path to Embryo Development Dataset", index_col=0)
...

Data Processing

Pre-process the DataFrames for calculating common gene-pathway pairs.

Compendium.reset_index(inplace=True)
Compendium.set_index(['WormBase','Term'],inplace=True)
...

Finding Common Gene-Pathway Pairs

Identify the common gene-pathway pairs across all datasets.

compemb=list(set(Compendium.index).intersection(set(EmbDev.index)))
...
Score['score']=float(-2)
...

Score Calculation

Compute the enrichment scores for each gene-pathway pair.

for genepathway in Score.index:
    count=0
    if genepathway in Compendium.index:
        count=count+1
    ...
    Score.loc[genepathway,'score']=count

Saving the Results

Save the calculated scores to a CSV file.

Score.to_csv("EnrichmentScoreAllDatasetsGeneID_052223.csv")

Output

The result is a CSV file containing the enrichment scores for all gene-pathway pairs derived from the analysis of the datasets.

Final Remarks

Ensure to adapt the file paths and names according to your local environment and dataset locations. A detailed description of each dataset, its format, and any prerequisite processing steps should be outlined. Any potential enhancements or improvements for the script can be addressed subsequently, and contributions for refining the script are welcomed.

For any issues, questions, or suggestions, please use the repository's issue tracker or submit a pull request.